Peripheral and cortical neural stimulation are increasingly popular disease treatments. These techniques function by delivering a pulse to a targeted area in order to create a neural response. For example, peripheral neural stimulation can be used to treat chronic pain and migraines. Recent research has explored employing peripheral neural stimulation with an eye towards developing closed loop control of prostheses in order to enhance prosthetic limb systems for amputees. Cortical neural stimulation can be used to treat Parkinson's disease and depression.
Recording neural signals during stimulation is important for examining the neural response and adjusting stimulation parameters accordingly. Most neural recording amplifiers presented in literature focus on size and noise efficiency factor. These measures are driven by the desire to scale up the number of electrodes while keeping both power and chip size reasonable for implantation. Many examples have the amplifier connected directly to the electrodes by a bonding or flip chip process. Artifacts from the environment and stimulation pulse often contaminate the recording and overlap the neural signal. This makes it necessary for implanted neural amplifiers to handle unwanted artifacts from several sources including 60 Hz (and harmonics), movement noise in peripheral applications, and stimulation artifacts in neural stimulation applications.
Previous methods of recording a neural signal in the presence of such artifacts have suffered from several disadvantages. For example, optimization of amplifier gain for noise with no consideration of artifacts results in impressive bench results but poor in-vivo results. Similarly, post-processing of the neural signal assumes very high dynamic range amplifiers, typically discrete devices on PCBs, and therefore may not be effective with implanted neural amplifiers. Therefore, there is a need in the art for improved systems and methods for recording neural signals in the presence of artifacts.